AIMC Topic: Particulate Matter
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Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.
The Science of the total environment
Dec 18, 2024
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...
Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM: Implications for Assessment of Health Impacts.
Environmental science & technology
Dec 18, 2024
Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wil...
Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.
Environment international
Dec 15, 2024
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....
Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.
PloS one
Dec 10, 2024
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...
Particulate matter estimation using satellite datasets: a machine learning approach.
Environmental science and pollution research international
Dec 3, 2024
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...
Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.
Waste management (New York, N.Y.)
Dec 1, 2024
Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM will enable the development of targeted strategies to reduce PM levels. This stud...
Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.
Environmental pollution (Barking, Essex : 1987)
Nov 28, 2024
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...
A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications.
Environmental monitoring and assessment
Nov 27, 2024
Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in...
MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.
Environmental monitoring and assessment
Nov 23, 2024
The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual ...
Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.
Environmental pollution (Barking, Essex : 1987)
Nov 22, 2024
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...
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